2 Data on External StorageDisks: Can retrieve random page at fixed costBut reading several consecutive pages is much cheaper than reading them in random orderTapes: Can only read pages in sequenceCheaper than disks; used for archival storageFile organization: Method of arranging a file of records on external storage.Record id (rid) is sufficient to physically locate recordIndexes are data structures that allow us to find the record ids of records with given values in index search key fieldsArchitecture: Buffer manager stages pages from external storage to main memory buffer pool. File and index layers make calls to the buffer manager. Page: typically 4 Kbytes.

3 Alternative File OrganizationsMany alternatives exist, each ideal for some situations, and not so good in others:Heap (random order) files: Suitable when typical access is a file scan retrieving all records.Sorted Files: Best if records must be retrieved in some order, or only a `range’ of records is needed.Indexes: Data structures to organize records via trees or hashing.Like sorted files, they speed up searches for a subset of records, based on values in certain (“search key”) fieldsUpdates are much faster than in sorted files.2

4 IndexesAn index on a file speeds up selections on the search key fields for the index.Any subset of the fields of a relation can be the search key for an index on the relation.Search key is not the same as key (minimal set of fields that uniquely identify a record in a relation).An index contains a collection of data entries, and supports efficient retrieval of all data entries k* with a given key value k.7

5 Index ClassificationPrimary vs. secondary: If search key contains primary key, then called primary index.Unique index: Search key contains a candidate key.Clustered vs. unclustered: If order of data records is the same as order of data entries, then called clustered index.A file can be clustered on at most one search key.Cost of retrieving data records through index varies greatly based on whether index is clustered or not!11

6 Index ClassificationDense vs Sparse: If there is an entry in the index for each key value -> dense index (unclustered indices are dense). If there is an entry for each page -> sparse index.15..BrownChenPetersonRhodesSmithYuWhite1 Brown ..2 Smith..3 White ..4 Yu ..5 Chen ..6 Peterson..7 Rhodes..………..11

9 Hash-Based Indexes Good for equality selections.Index is a collection of buckets. Bucket = primary page plus zero or more overflow pages.Hashing function h: h(r) = bucket in which record r belongs. h looks at the search key fields of r.Buckets may contain the data records or just the rids.Hash-based indexes are best for equality selections. Cannot support range searches2

11 Static Hashing (Contd.)Buckets contain data entries.Hash fn works on search key field of record r. Must distribute values over range M-1.h(key) = (a * key + b) usually works well.a and b are constants; lots known about how to tune h.4

12 Cost Model for Our AnalysisWe ignore CPU costs, for simplicity:B: The number of data pagesR: Number of records per pageD: (Average) time to read or write disk pageMeasuring number of page I/O’s ignores gains of pre-fetching a sequence of pages; thus, even I/O cost is only approximated.Average-case analysis; based on several simplistic assumptions.Good enough to show the overall trends!3

14 Operations to Compare Scan: Fetch all records from diskEquality searchRange selectionInsert a recordDelete a record

15 Cost of OperationsSeveral assumptions underlie these (rough) estimates!B: The number of data pagesR: Number of records per pageD: (Average) time to read or write disk page5

16 Choice of Indexes What indexes should we create?One approach: Consider the most important queries in turn. Consider the best plan using the current indexes, and see if a better plan is possible with an additional index. If so, create it.Obviously, this implies that we must understand how a DBMS evaluates queries and creates query evaluation plans!For now, we discuss simple 1-table queries.Before creating an index, must also consider the impact on updates in the workload!Trade-off: Indexes can make queries go faster, updates slower. Require disk space, too.13

17 Index Selection GuidelinesAttributes in WHERE clause are candidates for index keys.Exact match condition suggests hash index.Range query suggests tree index.Clustering is especially useful for range queries; can also help on equality queries if there are many duplicates.Multi-attribute search keys should be considered when a WHERE clause contains several conditions.Try to choose indexes that benefit as many queries as possible. Since only one index can be clustered per relation, choose it based on important queries that would benefit the most from clustering.14

18 Examples of Clustered IndexesB+ tree index on E.age can be used to get qualifying tuples.How selective is the condition?Is the index clustered?Consider the GROUP BY query.If many tuples have E.age > 10, using E.age index and sorting the retrieved tuples may be costly.Clustered E.dno index may be better!Equality queries and duplicates:Clustering on E.hobby helps!SELECT E.dnoFROM Emp EWHERE E.age>40SELECT E.dno, COUNT (*)FROM Emp EWHERE E.age>10GROUP BY E.dnoSELECT E.dnoFROM Emp EWHERE E.hobby=Stamps18

19 Indexes with Composite Search KeysComposite Search Keys: Search on a combination of fields.Equality query: Every field value is equal to a constant value. E.g. wrt <sal,age> index:age=20 and sal =75Range query: Some field value is not a constant. E.g.:age =20; or age=20 and sal > 10Data entries in index sorted by search key to support range queries.Order or attributes is relevant.Examples of composite key11,801112,1012nameagesal12,201213,75bob121013<age, sal>cal1180<age>joe122010,12sue13751020,12Data recordssorted by name2075,137580,1180<sal, age><sal>Data entries in indexsorted by <sal,age>Data entriessorted by <sal>13

20 Composite Search KeysTo retrieve Emp records with age=30 AND sal=4000, an index on <age,sal> would be better than an index on age or an index on sal.Choice of index key orthogonal to clustering etc.If condition is: 20<age<30 AND 3000<sal<5000:Clustered tree index on <age,sal> or <sal,age> is best.If condition is: age=30 AND 3000<sal<5000:Clustered <age,sal> index much better than <sal,age> index!Composite indexes are larger, updated more often.20

21 Summary (Contd.)Data entries can be actual data records, <key, rid> pairs, or <key, rid-list> pairs.Choice orthogonal to indexing technique used to locate data entries with a given key value.Can have several indexes on a given file of data records, each with a different search key.Indexes can be classified as clustered vs. unclustered, primary vs. secondary, and dense vs. sparse. Differences have important consequences for utility/performance.15

22 Summary (Contd.)Understanding the nature of the workload for the application, and the performance goals, is essential to developing a good design.What are the important queries and updates? What attributes/relations are involved?Indexes must be chosen to speed up important queries (and perhaps some updates!).Index maintenance overhead on updates to key fields.Choose indexes that can help many queries, if possible.Build indexes to support index-only strategies.Clustering is an important decision; only one index on a given relation can be clustered!Order of fields in composite index key can be important.